Peer review: An effective approach to cultivating lecturing virtuosity
Bibliographic record
Abstract
BACKGROUND: Most university faculty members are expected to teach. Many would benefit from instruction designed to improve lecturing. AIMS: To explore the impact of a program in which video-recorded lectures were critiqued by peers. METHOD: Sixteen lecturers participated in this qualitative study. Four agreed to have an undergraduate lecture video-recorded for peer review. Twelve participated in review sessions wherein the lecturer and three peers viewed and critiqued the recorded lecture. All discussions were recorded and transcribed for thematic analysis. Subsequently, semi-structured interviews were conducted with each lecturer and all 12 peer reviewers. Three pairs of research team members independently conducted thematic analyses of the discussion transcripts and the interviews; then all members met to develop consensus on major emergent themes. RESULTS: Six themes were identified: (1) the benefits of peer review; (2) the components of successful peer review; (3) the value of reflection on teaching experiences; (4) the inherent stress in peer evaluations; (5) the elements of successful lecturing; (6) lecturing as performance. CONCLUSIONS: The benefits of peer assessment of lecturing (PAL) were enthusiastically endorsed by all 16 participants. The PAL program is now supported by the McGill Faculty Development Committee and plans to implement regular PAL sessions in place.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".